Unless otherwise indicated, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
When developing program code for multiple computer operating systems, the program code is generic except for specific layers performing platform-dependent tasks. Generic program code should compile and run on all platforms. A core file is typically generated by the operating system when a process fails because of an irrecoverable error. Information obtained from this core file serves as a starting point for determining and analyzing what contributed to the failure.
Commercially available software programs are often shipped in an optimized format, without symbol and type information. In conventional debugging and analysis techniques, lack of this information can necessitate running a process multiple times. Rebuilding unoptimized code is extremely inefficient for software programs with a large source base. When it is not readily apparent how much of the code needs rebuilding, it is impractical to rebuild the code in its entirety because of the size of the resulting binary.
To circumvent this limitation, engineers manually inspect the optimized program code, trying to pinpoint areas that could have contributed to the error. Engineers will typically rebuild the suspect portion of the code unoptimized, run it in the same environment where the error occurred, and attempt to replicate the error. Time and inaccuracy are major drawbacks to this conventional debugging and analysis technique. In addition, the unoptimized code may not behave consistently with the optimized code because the behavior of the executable may be different, and therefore, the error may not be reproducible.
Support and development teams typically perform debugging in tandem. Platforms at client, development, and support sites may well vary, and core file formats vary from platform to platform. Additionally, byte ordering of data differs depending on machine architecture. There are many limitations to conventional debugging and analysis techniques.
For example, in most collaborative support and development environments, support teams are the first to receive and analyze core files generated by a software crash at a client site. Generally, development and support work together to assess, troubleshoot and resolve code errors. One benefit of collaborative environments is that individuals are able to contribute to areas of the code in which they have expertise. However, a drawback to traditional techniques is that collaborative environments often include multiple platforms, operating system versions, and environments. Traditional techniques can require support and development personnel to repeat steps in their separate environments. Both time and effort would be saved if developers and support analysts were able to contribute to editing and building code without duplicating effort. Incremental and persistent capture and storage of analysis and debugging data would save additional time and effort.
In addition, when platforms at client and development sites are different, replicating bugs may be difficult or even impossible. Conventional debuggers require a compiled binary for each platform. A drawback to traditional techniques is that even with platform-specific layers, there may be bugs on a specific platform that will not replicate on another platform. Traditional techniques require that the developer would have to replicate, change, test, and debug the code on both deployment and development environments. This approach requires that the developer be familiar with tools, debuggers and other support software on both platforms. If the developer could analyze the code in a generic format on any platform, time and effort would be saved.
It is therefore desirable to provide techniques for generating and representing core files in a generic format and performing analysis on existing core dumps from optimized binaries. Performing analysis on existing core dumps from optimized binaries eliminates replication of effort using optimized and unoptimized formats. Generating and representing core files in a generic format would remove platform dependence from the debugging process.